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Summary Objective The objective of this exploratory study was to determine if perturbations in gut microbial composition and the gut metabolome could be linked to individuals with obesity and osteoarthritis (OA). Methods Fecal samples were collected from obese individuals diagnosed with radiographic hand plus knee OA (n=59), defined as involvement of at least 3 joints across both hands, and a Kellgren-Lawrence (KL) grade 2-4 (or total knee replacement) in at least one knee. Controls (n=33) were without hand OA and with KL grade 0-1 knees. Fecal metabolomes were analyzed by a UHPLC/Q Exactive HFx mass spectrometer. Microbiome composition was determined in fecal samples by 16S ribosomal RNA amplicon sequencing (rRNA-seq). Stepwise logistic regression models were built to determine microbiome and/or metabolic characteristics of OA. Results Untargeted metabolomics analysis indicated that OA cases had significantly higher levels of di- and tripeptides and significant perturbations in microbial metabolites including propionic acid, indoles, and other tryptophan metabolites. Pathway analysis revealed several significantly perturbed pathways associated with OA including leukotriene metabolism, amino acid metabolism and fatty acid utilization. Logistic regression models selected metabolites associated with the gut microbiota and leaky gut syndrome as significant predictors of OA status, particularly when combined with the rRNA-seq data. Conclusions Adults with obesity and knee plus hand OA have distinct fecal metabolomes characterized by increased products of proteolysis, perturbations in leukotriene metabolism, and changes in microbial metabolites compared with controls. These metabolic perturbations indicate a possible role of dysregulated proteolysis in OA.

Opioid use disorder (OUD) is diagnosed using the qualitative criteria defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Diagnostic biomarkers for OUD do not currently exist. Our study focused on developing objective biological markers to differentiate chronic opiate users with OUD from chronic opiate users without OUD. Using biospecimens from the Golestan Cohort Study, we compared the metabolomics profiles of high opium users who were diagnosed as OUD positive with high opium users who were diagnosed as OUD negative. High opium use was defined as maximum weekly opium usage greater than or equal to the median usage (2.4 g per week), and OUD was defined as having 2 or more DSM-5 criteria in any 12-month period. Among the 218 high opium users in this study, 80 were diagnosed as OUD negative, while 138 were diagnosed as OUD positive. Seven hundred and twelve peaks differentiated high opium users diagnosed as OUD positive from high opium users diagnosed as OUD negative. Stepwise logistic regression modeling of subject characteristics data together with the 712 differentiating peaks revealed a signature that is 95% predictive of an OUD positive diagnosis, a significant (p

Understanding of causal biology and predictive biomarkers are lacking for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB). First-trimester serum specimens from 51 cases of HDP, including 18 cases of pre-eclampsia (PE) and 33 cases of gestational hypertension (GH); 53 cases of PTB; and 109 controls were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository. Metabotyping was conducted using liquid chromatography high resolution mass spectroscopy and nuclear magnetic resonance spectroscopy. Multivariable logistic regression was used to identify signals that differed between groups after controlling for confounders. Signals important to predicting HDP and PTB were matched to an in-house physical standards library and public databases. Pathway analysis was conducted using GeneGo MetaCore. Over 400 signals for endogenous and exogenous metabolites that differentiated cases and controls were identified or annotated, and models that included these signals produced substantial improvements in predictive power beyond models that only included known risk factors. Perturbations of the aminoacyl-tRNA biosynthesis, l-threonine, and renal secretion of organic electrolytes pathways were associated with both HDP and PTB, while pathways related to cholesterol transport and metabolism were associated with HDP. This untargeted metabolomics analysis identified signals and common pathways associated with pregnancy complications.

In utero and early life exposure to inorganic arsenic (iAs) alters immune response in experimental animals and is associated with an increased risk of infant infections. iAs exposure is related to differences in the gut microbiota diversity, community structure, and the relative abundance of individual microbial taxa both in laboratory and human studies. Metabolomics permits a direct measure of molecular products of microbial and host metabolic processes. We conducted NMR metabolomics analysis on infant stool samples and quantified the relative concentrations of 34 known microbial-related metabolites. We examined these metabolites in relation to both in utero and infant log(2) urinary total arsenic concentrations (utAs, the sum of iAs and iAs metabolites) collected at approximately 6 weeks of age using linear regression models, adjusted for infant sex, age at sample collection, type of delivery (vaginal vs. cesarean section), feeding mode (breast milk vs. any formula), and specific gravity. Increased fecal butyrate (b = 214.24), propionate (b = 518.33), cholate (b = 8.79), tryptophan (b= 14.23), asparagine (b = 28.80), isoleucine (b = 65.58), leucine (b = 95.91), malonate (b = 50.43), and uracil (b = 36.13), concentrations were associated with a doubling of infant utAs concentrations (p< 0.05). These associations were largely among infants who were formula fed. No clear associations were observed with maternal utAs and infant fecal metabolites. Metabolomic analyses of infant stool samples lend further evidence that the infant gut microbiota is sensitive to As exposure, and these effects may have functional consequences.

BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks:p = 0.056; 12 months:p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks:p = 0.376; 12 months:p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2values demonstrated poor predictive performance across all models assessed (avg: − 5.06% -- 6 weeks; − 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344–6 weeks; 0.265–12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

This study was conducted to investigate the influence of outer diameter (OD) and length (L) of multiwalled carbon nanotubes (MWCNTs) on biodistribution and the perturbation of endogenous metabolite profiles. Three different-sized carboxylated MWCNTs (NIEHS-12-2: L 0.5-2 μm, OD 10-20 nm, NIEHS-13-2: L 0.5-2 μm, OD 30-50 nm, and NIEHS-14-2: L 10-30 μm, OD 10-20 nm) in water were administered to female Sprague-Dawley rats as a single intravenous dose of 1 mg/kg MWCNTs. Biodistribution in liver, lung, spleen, and lymph nodes was evaluated in tissue sections at 1 and 7 days' post-dosing using enhanced darkfield microscopy and hyperspectral imaging. Nuclear magnetic resonance (NMR) analysis was used for biochemical profiling and pathway mapping of endogenous metabolites in urine collected at 24-h intervals prior to dosing, at Day 1 and Day 7. At Day 1 and Day 7, all three MWCNTs were observed in liver. NIEHS-12-2 was observed in spleen, whereas NIEHS-13-2 and NIEHS-14-2 were not. All three MWCNTs were observed in lymph nodes and lung at Day 7. The urinary biochemical profile showed the highest positive fold change (FC) at Day 7 for the metabolites acetate, alanine, and lactate, whereas 1-methylnicotinamide, 2-oxoglutarate, and hippurate had some of the lowest FCs for all three MWCNTs. This study demonstrates that the observed tissue location of MWCNTs is size dependent. Overlaps in the perturbation of endogenous metabolite profiles were found regardless of their size, and the biochemical responses were more profound at Day 7 compared with Day 1, indicating a delayed biological response to MWCNTs.

Background Metabolomics is a promising method to investigate physiological effects of chemical exposures during pregnancy, with the potential to clarify toxicological mechanisms, suggest sensitive endpoints, and identify novel biomarkers of exposures. Objective Investigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy. Methods Data were obtained from participants (n = 177) in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. Chemical exposures were assessed via silicone wristbands worn for one week at ~13 gestational weeks. Metabolomic features were assessed in plasma samples obtained at ~24-28 gestational weeks via the Biocrates AbsoluteIDQ® p180 kit and nuclear magnetic resonance (NMR) spectroscopy. Associations between chemical exposures and plasma metabolomics were investigated using multivariate modeling. Results Chemical exposures predicted 11 (of 226) and 23 (of 125) metabolomic features in Biocrates and NMR, respectively. The joint chemical exposures did not significantly predict pathway enrichment, though some individual chemicals were associated with certain amino acids and related metabolic pathways. For example, N,N-diethyl-m-toluamide was associated with the amino acids glycine, L-glutamic acid, L-asparagine, and L-aspartic acid and enrichment of the ammonia recycling pathway. Significance This study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.

Summary In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Objective:Over 50 million people worldwide are estimated to use opioids, of which ~30 million use opiates (opium and its derivatives). Use of opiates has been associated with a variety of adverse complications such as neurological and behavioral outcomes, addiction, cancers, diabetes, and cardiovascular disease. While it is well known that opiates exert their neurobiological effects through binding with mu, kappa, and delta receptors to exert analgesic and sedative effects, mechanistic links to other health effects are not well understood. Our study focuses on the identification of biochemical perturbations in Golestan Cohort Study (GCS) opium users.Methods:We used untargeted metabolomics to evaluate the metabolic profiles of 218 opium users and 80 non-users participating in the GCS. Urine samples were obtained from adult (age 40–75) opium users living in the Golestan Province of Iran. Untargeted analysis of urine was conducted using a UPLC-Q-Exactive HFx Mass Spectrometry and a 700 MHz NMR Spectrometry.Results:These GCS opium users had a significantly higher intake of tobacco and alcohol and a significantly decreased BMI compared with non-users. Metabolites derived from opium (codeine, morphine, and related glucuronides), nicotine, and curing or combustion of plant material were increased in opium users compared with non-users. Endogenous compounds which differentiated the opium users and non-users largely included vitamins and co-factors, metabolites involved in neurotransmission, Kreb's cycle, purine metabolism, central carbon metabolism, histone modification, and acetylation.Conclusions:Our study reveals biochemical perturbations in GCS opium users that are important to the development of intervention strategies to mitigate against the development of adverse effects of substance abuse.

BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationships within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 168) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis of sample distances (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg. R2: -5.06% -- 6 weeks; -3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg. correlation: 0.344 – 6 weeks; 0.265 – 12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

It has been suggested that medications can increase heat stroke (HS) susceptibility/severity. We investigated whether the nonsteroidal anti-inflammatory drug (NSAID) indomethacin (INDO) increases HS severity in a rodent model. Core temperature (Tc) of male, C57BL/6J mice ( n = 45) was monitored continuously, and mice were given a dose of INDO [low dose (LO) 1 mg/kg or high dose (HI) 5 mg/kg in flavored treat] or vehicle (flavored treat) before heating. HS animals were heated to 42.4°C and euthanized at three time points for histological, molecular, and metabolic analysis: onset of HS [maximal core temperature (Tc,Max)], 3 h of recovery [minimal core temperature or hypothermia depth (HYPO)], and 24 h of recovery (24 h). Nonheated (control) animals underwent identical treatment in the absence of heat. INDO (LO or HI) had no effect on physiological indicators of performance (e.g., time to Tc,Max, thermal area, or cooling time) during heating or recovery. HI INDO resulted in 45% mortality rate by 24 h (HI INDO + HS group). The gut showed dramatic increases in gross morphological hemorrhage in HI INDO + HS in both survivors and nonsurvivors. HI INDO + HS survivors had significantly lower red blood cell counts and hematocrit suggesting significant hemorrhage. In the liver, HS induced cell death at HYPO and increased inflammation at Tc,Max, HYPO, and 24 h; however, there was additional effect with INDO + HS group. Furthermore, the metabolic profile of the liver was disturbed by heat, but there was no additive effect of INDO + HS. This suggests that there is an increase in morbidity risk with INDO + HS, likely resulting from significant gut injury. NEW & NOTEWORTHY This paper suggests that in a translational mouse model, NSAIDs may be counterindicated in situations that put an individual at risk of heat injury. We show here that a small, single dose of the NSAID indomethacin before heat stroke has a dramatic and highly damaging effect on the gut, which ultimately leads to increased systemic morbidity.

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

This chapter introduces the field of metabolomics, covering aspects of study design, technologies, and analysis approaches that are used to reveal links between an individual’s metabotypes and states of health and wellness. Sample studies are described that link metabolic perturbations that are revealed in metabolomics studies with genetic polymorphisms and protein encoding genes. These studies point to metabolites that are important for nutritional intervention for the disease phenotypes.

Genetic polymorphisms associated with drug metabolism can significantly impair chemotherapeutic efficacy. Some African American women have poor treatment response to taxane-based chemotherapy drugs, which are widely used to treat breast cancer (BCa). Our study sought to identify gene-metabolite interactions to determine how selected polymorphisms impact treatment-relevant metabolic pathways that may contribute to poor response. This approach will enable more precise treatment based on genetically-defined metabolic drug-response as well as the consideration of ethnicity which can also play a role in disparate cancer outcomes. We treated a panel of four BCa cell lines derived from tumors taken from African American or Caucasian women with paclitaxel, at LD50 ≈ 100 ng/mL, determined by MTT assay across all lines. Cell pellets were collected for three replicate experiments for treated versus untreated conditions and UPLC high resolution mass spectrometry untargeted metabolomics was performed to identify significantly perturbed metabolic pathways that differentiate response to paclitaxel treatment. We observed differentiation between the four cell lines based on the specific metabolites that separated treated versus control/line using supervised multivariate analysis. Further, three endogenous pathways (Benzoate degradation via CoA ligation, Electron transport chain and Nucleotide sugar metabolism) were significantly perturbed (p Citation Format: Herman L. Freeman, Yuan-yuan Li, Susan L. McRitchie, Wimal W. Pathmasiri, Susan J. Sumner, Baba B. Mass, Brea C. Nance, Saroja Voruganti, Delisha Antionette Stewart. Identification of genetic and metabolic impairments to improve chemotherapeutic efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2355.

ONC201 is a novel anticancer compound currently in over 20 clinical trials for multiple different cancers, including triple negative breast cancer (TNBC). We recently demonstrated that ONC201 and the more potent TR compounds selectively activate the mitochondrial protease ClpP. While previous studies demonstrated substantial effects on mitochondrial metabolism, the consequences of ClpP activation on cancer cell metabolism are yet to be defined. To investigate this, we performed an unbiased metabolomics and proteomics analysis comparing the effects of ONC201 and TR-57 on the TNBC metabolome and proteome. Consistent with these compounds targeting the mitochondria, our studies identified multiple mitochondrial metabolites and enzymes that were impacted by ClpP activation, such as TCA cycle intermediates and enzymes, heat shock proteins and mitochondrial ribosomal proteins. In addition to previously reported elements of the integrated stress response (ATF4, CHOP), we identified several proteins that were upregulated as part of the mitochondrial-nuclear signaling process. qRT-PCR was used to determine the impact of ONC201 or TR-57 treatment on transcript levels of proteomic hits. Importantly, the effects of ONC201 and the TR compounds on these proteins and cell growth were abolished in ClpP null cells. In summary, our studies further demonstrate that loss of metabolic function in mitochondria contributes to the anticancer activity of these ClpP activator compounds. Citation Format: Emily M. Fennell, Lucas J. Aponte-Collazo, Blake R. Rushing, Yuan-Yuan Li, Wimal Pathmasiri, Joshua D. Wynn, Paul R. Graves, Ekhson L. Holmuhamedov, Laura E. Herring, Edwin J. Iwanowicz, Lee M. Graves. Disruption of mitochondrial metabolism by ClpP activation in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2398.

Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in Gómez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and % dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure.

Few investigations have been conducted on the disposition and fate of silver nanoparticles (AgNP) in pregnancy. The distribution of a single dose of polyvinylpyrrolidone (PVP)-stabilized AgNP was investigated in pregnant rats. Two sizes of AgNP, 20 and 110 nm, and silver acetate (AgAc) were used to investigate the role of AgNP diameter and particle dissolution in tissue distribution, internal dose and persistence. Dams were administered AgNP or AgAc intravenously (i.v.) (1 mg kg-1 ) or by gavage (p.o.) (10 mg kg-1 ), or vehicle alone, on gestation day 18 and euthanized at 24 or 48 h post-exposure. The silver concentration in tissues was measured using inductively-coupled plasma mass spectrometry. The distribution of silver in dams was influenced by route of administration and AgNP size. The highest concentration of silver (μg Ag g-1 tissue) at 48 h was found in the spleen for i.v. administered AgNP, and in the lungs for AgAc. At 48 h after p.o. administration of AgNP, the highest concentration was measured in the cecum and large intestine, and for AgAc in the placenta. Silver was detected in placenta and fetuses for all groups. Markers of cardiovascular injury, oxidative stress marker, cytokines and chemokines were not significantly elevated in exposed dams compared to vehicle-dosed control. NMR metabolomics analysis of urine indicated that AgNP and AgAc exposure impact the carbohydrate, and amino acid metabolism. This study demonstrates that silver crosses the placenta and is transferred to the fetus regardless of the form of silver. Copyright © 2016 John Wiley & Sons, Ltd.

The objective of this investigation was to determine differences in the profiles of endogenous metabolites (metabolomics) among ovaries and serum derived from Old World nonhuman primates fed prudent or Western diets.A retrospective, observational study was done using archived ovarian tissue and serum from midlife cynomolgus monkeys (Macaca fasicularis). Targeted and broad spectrum metabolomics analysis was used to compare ovarian tissue and serum from monkeys that had been exposed to a prudent diet or a Western diet. Monkeys in the prudent diet group (n=13) were research naïve and had been exposed only to a commercial monkey chow diet (low in cholesterol and saturated fats, high in complex carbohydrates). Western diet monkeys (n=8) had consumed a diet that was high in cholesterol, saturated animal fats and soluble carbohydrates for 2 years prior to ovarian tissue and serum collection.Metabolomic analyses were done on extracts of homogenized ovary tissue samples, and extracts of serum. Targeted analysis was conducted using the Biocrates p180 kit and broad spectrum analysis was conducted using UPLC-TOF-MS, resulting in the detection of 3500 compound ions.Using metabolomics methods, which capture thousands of signals for metabolites, 64 metabolites were identified in serum and 47 metabolites were identified in ovarian tissue that differed by diet. Quantitative targeted analysis revealed 13 amino acids, 6 acrylcarnitines, and 2 biogenic amines that were significantly (p0.05) different between the two diet groups for serum extracts, and similar results were observed for the ovary extracts.These data demonstrate that dietary exposure had a significant impact on the serum and ovarian metabolome, and demonstrated perturbation in carnitine, lipids/fatty acid, and amino acid metabolic pathways.

To date, no targeted therapies are available to treat triple negative breast cancer (TNBC), while other breast cancer subtypes are responsive to current therapeutic treatment. Metabolomics was conducted to reveal differences in two hormone receptor-negative TNBC cell lines and two hormone receptor-positive Luminal A cell lines. Studies were conducted in the presence and absence of paclitaxel (Taxol). TNBC cell lines had higher levels of amino acids, branched-chain amino acids, nucleotides, and nucleotide sugars and lower levels of proliferation-related metabolites like choline compared with Luminal A cell lines. In the presence of paclitaxel, each cell line showed unique metabolic responses, with some similarities by type. For example, in the Luminal A cell lines, levels of lactate and creatine decreased while certain choline metabolites and myo-inositol increased with paclitaxel. In the TNBC cell lines levels of glutamine, glutamate, and glutathione increased, whereas lysine, proline, and valine decreased in the presence of drug. Profiling secreted inflammatory cytokines in the conditioned media demonstrated a greater response to paclitaxel in the hormone-positive Luminal cells compared with a secretion profile that suggested greater drug resistance in the TNBC cells. The most significant differences distinguishing the cell types based on pathway enrichment analyses were related to amino acid, lipid and carbohydrate metabolism pathways, whereas several biological pathways were differentiated between the cell lines following treatment.

Summary Introduction Metabolic factors may contribute to osteoarthritis (OA). This study employed metabolomics analyses to determine if differences in metabolite profiles could distinguish people with knee OA who exhibited radiographic progression. Methods Urine samples obtained at baseline and 18 months from overweight and obese adults in the Intensive Diet and Exercise for Arthritis (IDEA) trial were selected from two subgroups (n = 22 each) for metabolomics analysis: a group that exhibited radiographic progression (≥0.7 mm decrease in joint space width, JSW) and an age, gender, and body mass index (BMI) matched group who did not progress (≤0.35 mm decrease in JSW). Multivariate analysis methods, including orthogonal partial least square discriminate analysis, were used to identify metabolite profiles that separated progressors and non-progressors. Plasma levels of IL-6 and C-reactive protein (CRP) were evaluated as inflammatory markers. Results Multivariate analysis of the binned metabolomics data distinguished progressors from non-progressors. Library matching revealed that glycolate, hippurate, and trigonelline were among the important metabolites for distinguishing progressors from non-progressors at baseline whereas alanine, N,N-dimethylglycine, glycolate, hippurate, histidine, and trigonelline, were among the metabolites that were important for the discrimination at 18 months. In non-progressors, IL-6 decreased from baseline to 18 months while IL-6 was unchanged in progressors; the change over time in IL-6 was significantly different between groups. Conclusion These findings support a role for metabolic factors in the progression of knee OA and suggest that measurement of metabolites could be useful to predict progression. Further investigation in a larger sample that would include targeted investigation of specific metabolites is warranted.

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Breast cancer (BCa) and prostate cancer (PCa) are two of the most commonly occurring invasive cancers in African American women and men, respectively. Although they arise in anatomically different organs with distinct physiological function, a unique feature of both cancers can be hormone-dependence and thus, remarkable underlying biological similarity has been observed between the two malignancies. For example, increased risk of male BCa after PCa incidence was reported previously, in a large population-based study. The purpose of this study was identification of biomarkers to improve early diagnosis in African Americans, leveraging metabolic commonalities and differences between these two cancers. Using untargeted 1H nuclear magnetic resonance (NMR) metabolomics we identified several common and unique metabolites and biological pathways in plasma samples from BCa and PCa patients compared to plasma samples from women and men with no diagnosis or family history of either type of cancer (controls). Samples were provided from the Tissue, Plasma and Clinical Bank at the Howard University Cancer Center (HUCC). Multivariate analysis demonstrated greater differentiation in the metabolic profiles between control plasma samples for both men and women, with an increase in the level of some amino acids (e.g. alanine, N-acetyl-tyrosine, asparagine, glutamine, histidine, tryptophan and tyrosine), but no differences between short chain fatty acids (2-hydroxyisovalerate, β-hydroxybutyrate). The analysis between BCa and PCa plasma profiles showed similar results. Comparison between control women versus BCa patients demonstrated decreases in all library-matched, significant metabolites except for increases in β-hydroxybutyrate and formate in patients. Analysis of control men and PCa patients showed increased levels of most amino acids but decreases in short chain fatty acids. Interestingly, pathway evaluation showed decreased glucose utilization in comparisons between both sets of control samples and cancer samples, individually, but no difference between controls versus cancer samples. Metabolically-relevant markers can serve as a viable means for earlier cancer detection, to have a major impact on cancer outcomes for African Americans, who suffer disparate burdens from these cancers, in-part because of delayed diagnoses and poor treatment efficacy associated with later-stage disease. Citation Format: Delisha A. Stewart, Wimal W. Pathmasiri, Susan L. McRitchie, Lance Buckley, Tammey J. Naab, Robert L. DeWitty, Jr, Vikisha T. Fripp, Desta A. Beyene, Olakunle O. Kassim, Yasmine M. Kanaan, Susan J. Sumner, Robert L. Copeland, Jr. Common and unique breast and prostate cancer metabolic profiles in African Americans [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A085.

Due to the surge in type 2 diabetes mellitus (T2DM), treatments for chronic metabolic dysregulations with fewer side-effects are sought. Lycii Cortex (LyC), a traditional Chinese Medicine (TCM) herb has a long history of being widely prescribed to treat T2DM as alternative medicine; however, the bioactive molecules and working mechanism remained unknown. Previous studies revealed kukoamine B (KB) as a major and featured compound for LyC with bioactivities for anti-oxidation and acute inflammation, which may be related to anti-diabetes properties. This study aims to understand the efficacy and the mode of action of KB in the diabetic (db/db) mouse model using a metabolomics approach. Parallel comparison was conducted using the first-line anti-diabetic drugs, metformin and rosligtazone, as positive controls. The db/db mice were treated with KB (50 mg kg−1 day−1) for 9 weeks. Bodyweight and fasting blood glucose were monitored every 5 and 7 days, respectively. Metabolomics and high-throughput molecular approaches, including lipidomics, targeted metabolomics (Biocrates p180), and cytokine profiling were applied to measure the alteration of serum metabolites and inflammatory biomarkers between different treatments vs. control (db/db mice treated with vehicle). After 9 weeks of treatment, KB lowered blood glucose, without the adverse effects of bodyweight gain and hepatomegaly shown after rosiglitazone treatment. Lipidomics analysis revealed that KB reduced levels of circulating triglycerides, cholesterol, phosphatidylethanolamine, and increased levels of phosphatidylcholines. KB also increased acylcarnitines, and reduced systemic inflammation (cytokine array). Pathway analysis suggested that KB may regulate nuclear transcription factors (e.g., NF-κB and/or PPAR) to reduce inflammation and facilitate a shift toward metabolic and inflammatory homeostasis. Comparison of KB with first-line drugs suggests that rosiglitazone may over-regulate lipid metabolism and anti-inflammatory responses, which may be associated with adverse side effects, while metformin had less impact on lipid and anti-inflammation profiles. Our research from holistic and systemic views supports the conclusion that KB is the bioactive compound of LyC for managing T2DM, and suggests KB as a nutraceutical or a pharmaceutical candidate for T2D treatment. In addition, our research provides insights related to metformin and rosiglitazone action, beyond lowering blood glucose.

With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children’s Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQSRS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (1H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).

The early-life intestinal microbiota plays a key role in shaping host immune system development. We found that a single early-life antibiotic course (1PAT) accelerated type 1 diabetes (T1D) development in male NOD mice. The single course had deep and persistent effects on the intestinal microbiome, leading to altered cecal, hepatic, and serum metabolites. The exposure elicited sex-specific effects on chromatin states in the ileum and liver and perturbed ileal gene expression, altering normal maturational patterns. The global signature changes included specific genes controlling both innate and adaptive immunity. Microbiome analysis revealed four taxa each that potentially protect against or accelerate T1D onset, that were linked in a network model to specific differences in ileal gene expression. This simplified animal model reveals multiple potential pathways to understand pathogenesis by which early-life gut microbiome perturbations alter a global suite of intestinal responses, contributing to the accelerated and enhanced T1D development., eLife digest The human body contains many microbes that play important roles in our health. These microbes begin to live in the intestines, skin, and mouth shortly after birth. They form complex communities called the microbiome, which changes as babies develop. The microbiome works with organs to maintain human health. For example, the lower intestinal tract is home to the most numerous and active microbes in the body. The intestines provide microbes with food and a welcoming environment, and the microbes make products the body needs, influence immune system development, and help maintain a balance of beneficial microbes. Use of antibiotics to treat infections, particularly early in life, disrupts intestinal microbe communities. Recent studies show that such microbiome disturbances may affect how the immune system develops and the rate at which type 1 diabetes develops. Type 1 diabetes is an autoimmune disease in which the immune system destroys cells in the pancreas that produce insulin. Scientists would like to learn more about how use of antibiotics in early life may contribute to the development of this disease. Now, Zhang et al. show that a single course of antibiotics administered early in life accelerates the development of type 1 diabetes in mice prone to develop the disease. In the experiments, a strain of laboratory mice that spontaneously develops type 1 diabetes were either given a single course of antibiotics, three courses of antibiotics, or no antibiotics in their first weeks of life. After one single course, the gut microbiome was different in mice treated with antibiotics compared with mice who were never exposed. The antibiotics also changed the molecules produced by these microbes. These alterations in the microbiome turned on or off certain genes in the intestine, affecting the development of the immune system. Zhang et al. identified some microbes that appear to protect against type 1 diabetes and others that seem to speed it up and how they do so. Antibiotic use in children is very common, so finding ways to reduce its potentially harmful effects on development are critical. The experiments provide one way to study how antibiotics may contribute to autoimmune disease. It also may allow scientists to test ways to reverse harmful change.